Advanced GPU Strategies for Efficient AI Clusters

Integrating an NVIDIA H100 or A100 into your cluster today quickly reveals that the classic 1-to-1 allocation (one pod reserves an entire GPU) often results in massive capital waste in a production environment. While training LLMs fully utilizes the hardware, GPUs often idle at 10% utilization during inference operations or in development environments.

To reduce the TCO (Total Cost of Ownership) of your AI infrastructure, we must move beyond simple allocation and delve deep into Resource Management.

1. Fractional GPUs: Three Ways to Share Hardware

To allow multiple pods to share a physical GPU without interfering with each other, there are three established technical approaches today:

A. NVIDIA Multi-Instance GPU (MIG) – The Hard Separation

MIG allows a GPU to be divided into up to seven independent instances at the hardware level.

• Advantage: Each instance has its own dedicated memory and cache. A crash in Instance A does not affect Instance B.
• Use Case: Ideal for multi-tenant environments in mid-sized businesses where different departments require guaranteed performance.

B. Time-Slicing – The Pragmatic Approach

Here, Kubernetes uses the classic scheduler approach: Multiple processes use the GPU sequentially in extremely short time intervals.

• Advantage: Works with older or smaller GPUs (e.g., T4 or L40S) that do not support MIG.
• Use Case: Perfect for development clusters or simple inference workloads (e.g., image classification) that do not require strict latency guarantees.

C. GPU Oversubscription with CUDA MPS

Multi-Process Service (MPS) allows multiple processes to execute kernels on the GPU simultaneously.

• Advantage: Higher throughput rates than time-slicing, as computational units are better utilized.
• Disadvantage: Lower isolation. A memory error in one pod can affect all other processes on the GPU.

2. The Shift to Dynamic Resource Allocation (DRA)

A technical bottleneck in Kubernetes for a long time was the Device Plugin Framework. It treated GPUs as “countable units” (integers). With the introduction of Dynamic Resource Allocation (DRA) in newer K8s versions, the game changes fundamentally.

DRA allows us to define resources much more flexibly. Instead of just saying “I need a GPU,” we can specify complex requirements: “I need a GPU with at least 40GB VRAM and NVLink connectivity to the neighboring node.” This is essential for modern AI Superclusters, where network latency between GPUs (RDMA/RoCE) is as crucial as computational power itself.

3. Scheduling Intelligence with Kueue and Karpenter

Hardware sharing is only half the battle. The other half is Queue Management.

If three teams want to train a model simultaneously, but only two GPUs are available, the cluster must not simply run “Out of Memory.” Here, we rely on Kueue. It acts as a job queue manager on top of Kubernetes, deciding based on priorities and budgets which workload gets access to the expensive hardware and when.

In combination with Karpenter (instead of the standard Cluster Autoscaler), we can also ensure that we provision exactly the node types that are most cost-effective for the specific job – for example, spot instances for non-critical batch jobs.

Conclusion: Efficiency is No Accident

AI infrastructure in mid-sized businesses today means: Maximizing investment returns. Simply “passing through” GPUs costs too much. Only by combining hardware partitioning (MIG), modern resource scheduling (DRA), and intelligent queue management does your cluster become a true AI factory.

Technical FAQ: Deep Dive GPU Orchestration

What is the difference between MIG and vGPU? NVIDIA vGPU is a software-based solution often used in virtualization environments (VDI) and requires licenses per user. MIG is a hardware feature of newer Tensor-Core GPUs (Ampere architecture and newer), partitioned directly on the chip, and does not incur additional licensing fees within Kubernetes.

When should I avoid GPU sharing? During large model training (e.g., fine-tuning a Llama-3-70B). Here, you need the full memory bandwidth and entire VRAM of one or more GPUs. Any partitioning would significantly slow down the process or cause it to crash.

How do I monitor actual GPU utilization? Do not rely on standard K8s metrics. You need the NVIDIA DCGM Exporter, which provides metrics like “GPU Utilization,” “FB Memory Usage,” and even temperature directly into your Prometheus/VictoriaMetrics setup.

Is your GPU hardware optimally utilized? The architecture determines your cloud bill. At ayedo, we analyze your workloads and implement the appropriate sharing and scheduling strategies to maximize your performance and minimize costs.